The present invention is directed to a quantizer for compressing an amount of information of digital signals and also to a coder/decoder employing this quantizer.
Referring to FIG. 5, there is illustrated a prior art adaptive quantization coder/decoder reported in, e.g., [A Proposal of Coding Control Method for MC.DCT Coding Scheme] written by Kato et al (The National Convention of Information and Systems Group; The Institute of Electronic, Information and Communication Engineers, 1987). In FIG. 5, the numeral 1 designates a subtracter; 2 a transform unit; 3 a quantization coder; 4 a coding unit; 5 a quantization decoder; 6 an inverse transform unit; 7 an adder; 8 a frame memory; 9 an adaptive coding control unit; 10 a transmission channel; and 11 a decoding unit.
Operations of the system depicted in FIG. 5 will next be described. On the transmitting side, a difference signal between a digitized input image signal 100 and a predictive signal 101 which is a previous frame signal of the frame memory 8 is obtained by the subtracter 1. The thus obtained difference signal is defined as a predictive error signal 102. The predictive error signal 102 is transformed into a transform coefficient 103 of a frequency domain in the transform unit 2 by a transform function such as, e.g., a discrete cosine transform. The transform coefficient 103 is quantized to a discrete level (hereinafter referred to as a quantization level) 105 by the quantization coder 3 in accordance with a quantization step-size 104 provided by the adaptive coding control unit 9. A code is allocated to the quantization level 105 by means of the coding unit 4, as a result of which coded data 106 is transmitted together with information of the quantization step-size 104 to the transmission channel 10. The transform unit 2, the quantization coder 3 and the coding unit 4 are combined to constitute a quantization coding module. A decode transform coefficient 107 is obtained from the quantization level 105 by the quantization decoder 5, employing the quantization step-size 104. The decode transform coefficient 107 undergoes an inverse transform in the inverse transform unit 6 to thereby, obtain a decode predictive error signal 108. A local quantization decoding module is composed of the quantization decoder 5 and the inverse transform unit 6 on the transmitting side. The decode predictive error signals 108 is added to the predictive signal 101 by the adder 7. The added value is held as a local decode signal 109 in the frame memory 8 to be used as a predictive signal 101 of the next frame.
On the other hand, coding data 110 transmitted via the transmission channel 10 is decoded to a quantization level 111 by the decoding unit 11 on the receiving side. A decode transform coefficient 112 is obtained from the quantization level 111 by means of the quantization decoder 5, employing the quantization step-size 104 given from the decoding unit 11. The decode transform coefficient 112 undergoes an inverse transform in the inverse transform unit 6, thus acquiring a decode predictive error signal 113. A quantization decoding module consists of the decoding unit 11, the quantization decoder 5 and the inverse transform unit 6 on the receiving side. The decode predictive error signal 113 is added to a predictive signal 114 by the adder 7. The thus added signal is outputted as a decode signal 115 and at the same moment held in the frame memory 8 to be used as a predictive signal 114 of the next frame.
The quantization coder 3 for effecting the quantization as the quantization step-size 104 is adaptively controlled will hereinafter be described. Each dynamic range of the digitized input image signal 100 and the predictive signal 101 is from 0 to 255, i.e., 8 bits. In this case, the dynamic range of the predictive error signal 102 is from -255 to 255, viz., 9 bits (1 bit of which is a sign bit). The 9 bit predictive error signal 102 is arranged in (8.times.8) blocks and transformed into the transform coefficient 103 of the frequency domain of a 2 dimensional discrete cosine transform. In consequence of this, the dynamic range of the transform coefficient 103 ranges from -2048 to 2047, i.e., 12 bits (1 bit of which is a sign bit). Hence, the dynamic ranges of the input signal to the quantization coder 3 and of the output signal from the quantization decoder 5 are from -2048 to 2047. It is now assumed that a characteristic of the quantizer is defined, for simplicity, as a linear quantization characteristic of a mid-tread type wherein the quantization step-size shown in the following formula (1) is constant at all the decision levels. EQU q.sub.dec (n)=(.vertline.n .vertline..times.g).times.n/.vertline.n.vertline. EQU q.sub.rep (n)=1/2{q.sub.dec (n)+q.sub.dec (n+n/.vertline.n.vertline.)} EQU q.sub.rep (O)=0 (1)
where q.sub.dec (n) is the decision level, q.sub.rep (n) is the reconstruction value, g is the quantization step-size, and n is the quantization index. Here, g is the positive even number. Turning to FIG. 6, there is shown a quantization characteristic in this case. In FIG. 6, the horizontal axis indicates the decision level of the quantizer, while the vertical axis indicates the reconstruction value. For example, when the transform coefficient CO is prescribed such as 3 g.ltoreq.CO&lt;4 g, CO is quantized to 3.5 g, and the quantization index, which undergoes a coding transmission, becomes 3. More specifically, as illustrated in FIG. 7, when the quantization step-size g is set to 32, the transform coefficient CO, which is prescribed such as 64.ltoreq.CO&lt;96, is quantized to a reconstruction value 80. A magnitude of the quantization step-size corresponds to the fineness of the quantization. The quantization becomes less fine with the increasing quantization step-size g. A difference (quantization error) between the input value and the reconstruction value increases, thereby causing a deterioration in the quality of the decoded image. The dynamic range of the input value is, as discussed above, fixed. Therefore, when the quantization step-size g is large, the dynamic range of the coded quantization index decreases to thereby reduce the amount of information to be transmitted. When the quantization step-size g is, e.g., 16, the dynamic range of the quantization index ranges from -128 to 127. In contrast, when the quantization step-size g is 64, the dynamic range of the quantization index is from -32 to 31. Hence, it is possible to optimize the relationship between the quality of the decoded image and the amount of information to be transmitted by adaptively controlling the quantization step-size g in accordance with the image inputted.
Supposing that the quantization step-size g is varied and set to 30, a quantization characteristic at that time is shown in FIG. 8. Namely, the transform coefficient CO, which is prescribed such as 2040.ltoreq.CO.ltoreq.2047, is quantized to a reconstruction value 2055. In the case of being negative, the transform coefficient CO, which is prescribed such as -2048.ltoreq.CO.ltoreq.2040, is similarly quantized to a reconstruction value -2055.
There arises, however, the following problem inherent in the prior art adaptive quantization coder/decoder having the above-described construction. There exists a possibility that the reconstruction value outputted when changing the quantization step-size exceeds an allowable range of inputting at the next stage. For instance, a value-outputted from the quantization decoder in the quantization decoding module rises beyond the allowable range, resulting in a failure of operation.